Abstract
The recurrent neural networks of generalized architecture (GARNN) are general continuous dynamic systems. It was shown elsewhere that they can successfully manage the problem of on-line inference of finite automata. In addition, they can successfully solve problems of a continuous nature because they are continuous systems. A frequently used problem domain to check this are the well-known trajectory tracking problems. Some new problems of this problem domain are defined in this paper. The experiments are carried out with the generalized recurrent neural networks and solutions are found for each trajectory of the problem domain.
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References
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Gabrijel, I., Dobnikar, A. (2003). Generalized recurrent neural networks and continuous dynamic systems. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_2
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DOI: https://doi.org/10.1007/978-3-7091-0646-4_2
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-00743-3
Online ISBN: 978-3-7091-0646-4
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